{"id":"W2954157033","doi":"10.1080/08927022.2019.1632448","title":"Predicting CO<sub>2</sub> adsorption and reactivity on transition metal surfaces using popular density functional theory methods","year":2019,"lang":"en","type":"article","venue":"Molecular Simulation","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Supercomputing Centre Singapore; National Research Foundation; Western Canada Research Grid; Nanyang Technological University; National Research Foundation Singapore; Compute Canada","keywords":"Adsorption; Density functional theory; Chemistry; Molecule; Thermodynamics; Binding energy; Work (physics); Phase (matter); Reactivity (psychology); Physical chemistry; Metal; Computational chemistry; Atomic physics; Organic chemistry; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002623989,0.0007881171,0.0004760673,0.0004661965,0.0004552492,0.0005582918,0.0008497782,0.001282533,0.001683617],"category_scores_gemma":[0.0006611389,0.0002604865,0.0004495939,0.0005596877,0.0003046296,0.0004771604,0.0002247065,0.0004326853,0.0003684539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007803984,"about_ca_system_score_gemma":0.0006282045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005302883,"about_ca_topic_score_gemma":0.005255051,"domain_scores_codex":[0.9998995,0.00002471877,0.000003192231,0.0000113833,0.00003990113,0.00002121873],"domain_scores_gemma":[0.999718,0.0001784103,0.00001649155,0.00002403376,0.00004902204,0.00001412427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009151578,0.0001160686,0.00254215,0.0002499499,0.00008306574,0.0001622086,0.00005192879,0.9653438,0.01402364,0.006623349,0.000818247,0.009894197],"study_design_scores_gemma":[0.000003674538,0.00001289489,0.0003000768,0.000003014176,0.000003580921,0.00001064256,0.00001037034,0.9958771,0.003092132,0.0004575812,0.0002253663,0.000003432459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8877741,0.00119209,0.09344917,0.0004284565,0.00007571203,0.00007094027,0.000832809,0.0009846298,0.01519206],"genre_scores_gemma":[0.9765742,0.0003564968,0.02061557,0.00005623846,0.00001432608,0.0001191287,0.0005413257,0.0001221438,0.001600633],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005302883,"threshold_uncertainty_score":0.010544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205713507866011,"score_gpt":0.2933212746098626,"score_spread":0.2727499238232615,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}